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@NicolasCARPi
NicolasCARPi / LICENSE
Last active September 1, 2026 12:13
VibeCoded AI-Slop License v1.0
VibeCoded AI-Slop License v1.0
Copyright (c) [YEAR] [AUTHOR]
This software, source code, documentation, configuration, generated artifacts,
and whatever else happened to end up in this repository (collectively, the
"Software") is released under the VibeCoded AI-Slop License v1.0.
1. Permission
@franklai
franklai / 電影票價 (2026-01).md
Created January 5, 2026 00:05
電影票價 (2026-01)

電影票價 (2026-01)

影城 全票 早場
台北信義威秀 360 300
台北南港LaLaport威秀 360 300
台北京站威秀 360 300
台北西門威秀 320 260
新店裕隆城威秀 350 300
板橋大遠百威秀 350 300
@nguyen-phillip
nguyen-phillip / proc.c
Last active September 1, 2026 12:08
Using libproc.h
#include <stdio.h>
#include <stdlib.h>
#include <libproc.h>
// Uses proc_pidinfo from libproc.h to find the parent of given pid.
// Call this repeatedly until ppid(pid) == pid to get ancestors.
int ppid(pid_t pid) {
struct proc_bsdinfo info;
proc_pidinfo(pid, PROC_PIDTBSDINFO, 0, &info, sizeof(info));
return info.pbi_ppid;
===============================================================================
User Name-: Hardik
Registration code :- TZXZT-USMCB-ZRKYP-MTVG3-JM8UL
===============================================================================
@komikndr
komikndr / llamacpp-opencode.md
Created August 18, 2026 04:12
This is my OpenCode setup for local models, mainly using a customized llama.cpp configuration.

What it is

This is my OpenCode setup for local models, mainly using a customized llama.cpp configuration.

For Qwen 3.8, DeepSeek V4, and Glimmer, the models are already trained to support reasoning effort levels. Depending on the model, these may be exposed as low, medium, high, xhigh, or as low, high, and max.

For models that support reasoning but were not trained with explicit reasoning-effort levels, such as the Qwen 3.5 and 3.6 variants, I use a token budget to limit the amount of reasoning.

Although Qwen 3.8 has built-in reasoning-effort levels, I still apply a maximum reasoning-token cap for each effort level.

Although the llama.cpp CLI flags specify preserve_thinking and a default reasoning budget, these can still be overridden through the API, so this works fine for my setup.

@TheAngryByrd
TheAngryByrd / CyclomaticComplexity.fsx
Created January 8, 2025 13:52
CyclomaticComplexity.fsx
#r "nuget: FSharp.Compiler.Service, 43.8.300"
open FSharp.Compiler.Syntax
open FSharp.Compiler.SyntaxTrivia
open FSharp.Compiler.Xml
open FSharp.Compiler.CodeAnalysis
open System.IO
type Node =
{ Data : Data
@alexziskind1
alexziskind1 / gist:fe55f03892f3fe1d6d8cf6065c631bb8
Created August 6, 2026 17:02
Compute exchange front end prompt
Create a production-ready, visually stunning **front-end web app**.
GOAL
Build a single-page application (plus detail routes) for a fictional company:
**"SILICON EXCHANGE"** — a marketplace where people rent out idle GPUs and AI
accelerators by the hour. Renters browse listings, inspect live utilization
charts, and reserve time blocks.
FRONT END ONLY. No backend, no database, no auth server, no API keys. All data
is mock data defined in code. But the app must behave like the real thing — the
@code-boxx
code-boxx / 0-PHP-WEBAUTHN.MD
Last active September 1, 2026 11:54
Simple PHP Web Authn Example
@mfd
mfd / teams.sh
Last active September 1, 2026 11:54
Download any video from Microsoft Teams, SharePoint and OneDrive
2teams() {
NOW=$(date +"%Y-%m-%d_%H%M")
if [ ! -z $2 ] ; then
echo $NOW"_"$2.mp4
ffmpeg -i $1 -codec copy $NOW"_"$2.mp4
else
echo $NOW"_teamsvid".mp4
ffmpeg -i $1 -codec copy $NOW"_teamsvideo".mp4
fi
}

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.